The input to online reconstruction is an RGB-D sequence \(\lbrace I_c^t, I_d^t\rbrace _{t=0:T}\)  (\(I_c\)  and \(I_d\)  are RGB and depth images, respectively) captured by an RGB-D camera and the output is a surface reconstruction of the scene being captured, \(\mathcal {S}\) , and a trajectory of 6DoF camera poses, \(\lbrace [\mathbf {R}^t|\mathbf {t}^t]\rbrace _{t=0:T}\)  (\(\mathbf {R}\in SO(3)\)  and \(\mathbf {t}\in \mathbb {R}^3\)  are 3D rotation and 3D translation in the global coordinate system, respectively). We build our method upon the volumetric depth fusion framework [1]}, [2]} which is the de facto method for high-quality online dense reconstruction.
The key problem of online reconstruction is the estimation of 6D camera pose for each frame. Based on the per-frame camera pose, the corresponding depth map can be fused into the 3D volume incrementally.
To handle fast camera motion, we rely only on depth input for camera pose tracking, which is our key contribution. Of course, our method could admit RGB image input for camera tracking and relocalization when the camera motion is slow and image features are robustly detected.
